In recent years, the research and development of ITS (Intelligent Transport System) for practical use have been rapidly expanded along with the development and advancement of information processing technology, and more attention has been paid to the ITS as a new market by people in the industry.
Drive assist and navigation systems play a main role in the ITS. Here, it is desired that assistance and information presentation be made according to not only road and traffic situations and car behaviors but also characteristics and current state of a driver. Especially, estimation of the arousal level of a driver has long been studied. There have been conducted many researches using biological and physiological reaction such as brain waves, skin electrical activity, heart rate, and eye blink.
In particular, a blink occurrence pattern and the parameters of a waveform indicating an eye movement are known to vary according to the arousal level of an object person. Researches on detection of driver's drowsiness by use of the blink occurrence pattern and the parameters of the waveform indicating the eye movement at the time of blinking are now progressing.
Patent Document 1 discloses a behavior content classification device as a technique for classifying an arousal state by using the parameters of the waveform indicating the eye movement at the time of blinking.
The behavior content classification device includes an eye state classifying HMM (Hidden Markov Model) which outputs a likelihood for the type of blink waveform of an object person in relation to an input of features for video data of plural frames of an eye portion, and classifies the arousal state of the object person based on the likelihood output from the eye state classifying HMM in relation to the input of the features. A standard electro-oculogram (EOG) waveform in an arousal state and a typical electro-oculogram (EOG) waveform in a sleepy state are disclosed as the blink waveform associated with the arousal state.
In the related art disclosed in Patent Document 1, the type of the blink waveform is classified based on the parameters of the aperture, duration time and speed of a blink in each blink waveform at the time of generating the eye state classifying HMM.
Identification information for allowing identification of the type of the blink waveform is provided to the features extracted from the video data of plural frames of the eye portion corresponding to the blink waveform based on the classification result.
In addition, the HMM is learned by using the features to which the identification information is provided as described above as learning data.
Then, the HMM is generated for specific types of blinks, with which the arousal state can be sufficiently classified, out of various types of blink waveforms. The arousal state is classified based on a change in the occurrence frequencies of the specific types of blinks within a predetermined time.
To be more specific, the identification result of each blink waveform is histogram-processed in a predetermined time interval, thereby detecting the occurrence frequencies of blink patterns. When the occurrence frequency of a blink waveform that is not identified as the standard blink waveform in an arousal state becomes high, the arousal state is classified to become a low level.
Patent Document 1: International Publication No. WO2005/114576